system

Abstract

The system according to the embodiment aims to enable customers to narrow down their product selection based on their preferences and needs, and to efficiently create business negotiation opportunities with salespeople. [Solution] A system according to an embodiment includes a selection unit, a narrowing-down unit, an output unit, and a calling unit. The selection unit selects elements that a customer likes or needs. The narrowing-down unit narrows down products based on the elements selected by the selection unit. The output unit outputs the results narrowed down by the narrowing-down unit. The calling unit calls a salesperson based on the results output by the output unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult for customers to narrow down products based on their preferences and needs, making it difficult to efficiently create sales opportunities with salespeople.

[0005] The system according to the embodiment aims to enable customers to narrow down their product selection based on their preferences and needs, and to efficiently create business negotiation opportunities with salespeople. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a narrowing-down unit, an output unit, and a calling unit. The selection unit selects elements that a customer likes or needs. The narrowing-down unit narrows down products based on the elements selected by the selection unit. The output unit outputs the results narrowed down by the narrowing-down unit. The calling unit calls a salesperson based on the results output by the output unit. [Effects of the Invention]

[0007] The system according to the embodiment allows customers to narrow down products based on their preferences and needs, and efficiently create business negotiation opportunities with salespeople. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A sales support system according to an embodiment of the present invention uses a kiosk terminal installed in a store. A customer selects their preferences and necessary factors, prints the results on paper, etc., and then calls a salesperson to negotiate. This sales support system begins when a customer accesses a kiosk terminal and selects their preferences and necessary factors. Based on the selected factors, the kiosk terminal then narrows down the search to suitable products and prints the results on paper, etc. Based on the printed results, the customer can call a salesperson as needed to negotiate. This system allows customers to easily narrow down their search to the products they need, thereby reducing negotiation time for the store and increasing sales opportunities. For example, a customer accesses a kiosk terminal and selects their preferences and necessary factors. The kiosk terminal provides an interface, such as a touch panel or voice input, to facilitate customer operation. For example, the customer can select factors such as "price range," "brand," and "function." Based on the selected factors, the kiosk terminal then narrows down the search to suitable products. The kiosk terminal then accesses an internal database to search for products matching the selected factors. For example, if a customer selects "price range: ¥10,000 to ¥20,000," "brand: company A," and "function: waterproof," the system narrows down the search to products that match these criteria. The narrowed-down product results are printed out on paper or other media. Based on the results, customers can call a salesperson in the store as needed. For example, a kiosk terminal may have a "call salesperson" button, which can be pressed to call a salesperson. This system allows customers to easily narrow down the search to the products they need. For example, if a customer is looking for a product that meets specific criteria, they can use the kiosk terminal to quickly find the right product. It also reduces sales negotiation time and increases sales opportunities for stores. For example, salespeople no longer need to spend a long time with one customer, allowing them to attend to other customers. This allows the sales support system to allow customers to select their preferences and needs and narrow down the search to the right product.

[0029] A sales support system according to an embodiment includes a selection unit, a narrowing-down unit, an output unit, and a calling unit. The selection unit selects customer preferences or necessary factors. Examples of customer preferences or necessary factors include, but are not limited to, color, size, functionality, and price range. The selection unit provides an interface, such as a touch panel, voice input, or two-dimensional code (e.g., QR code (Wataka trademark)) scanning. For example, the selection unit uses a capacitive touch panel to allow customers to make selections by touching the screen. The selection unit can also accept customer voice input using voice recognition technology. Furthermore, the selection unit can also use two-dimensional code scanning to allow customers to make selections by scanning a two-dimensional code with their smartphone. The narrowing-down unit narrows down the products based on the factors selected by the selection unit. For example, the narrowing-down unit uses AI to analyze the customer's selection history and past purchase history. For example, the narrowing-down unit uses a machine learning algorithm to analyze the customer's selection history and suggest optimal products. The narrowing-down unit can also use deep learning technology to analyze the customer's past purchase history and suggest related products. Furthermore, the narrowing-down unit can set filtering conditions and search for products that match the selected elements. For example, the narrowing-down unit sets filtering conditions such as price range, brand, and function, and narrows down the products that match these conditions. The output unit outputs the results narrowed down by the narrowing-down unit. For example, the output unit has a function not only to output the results on paper but also to send them directly to a smartphone. For example, the output unit prints the results on paper using a printer. The output unit can also send the results to the smartphone by email or app notification. Furthermore, the output unit has a cloud unit that uses a cloud-based database and can output the results using data stored on the cloud. The calling unit calls a salesperson based on the results output by the output unit. For example, the calling unit has a function to send a notification to the salesperson's smartphone. For example, the calling unit sends the notification to the salesperson using a push notification or SMS notification. The calling unit can also send an app notification to the salesperson's smartphone.As a result, the sales support system according to the embodiment allows customers to select their preferences and necessary factors and narrow down the search to find suitable products. For example, if a customer is looking for a product that meets specific criteria, the sales support system can be used to find the appropriate product in a short amount of time. Furthermore, stores can shorten negotiation time and increase sales opportunities. For example, salespeople no longer need to spend a long time with one customer, and can instead attend to other customers.

[0030] The selection unit can provide an interface for a touch panel, voice input, or two-dimensional code scanning. The selection unit can use, for example, a capacitive touch panel to allow a customer to make a selection by touching the screen. For example, the selection unit can use a capacitive touch panel to allow a customer to make a selection by touching the screen. The selection unit can also use voice recognition technology to accept a customer's voice input. For example, the selection unit can use voice recognition technology to accept a customer's voice input. The selection unit can also use two-dimensional code scanning to allow a customer to make a selection by scanning a two-dimensional code with a smartphone. For example, the selection unit can use two-dimensional code scanning to allow a customer to make a selection by scanning a two-dimensional code with a smartphone. This makes it possible to provide an interface that is easy for customers to operate. Some or all of the above-described processing in the selection unit can be performed using, for example, AI, or without AI. For example, the selection unit can input a customer's voice input to a generation AI and have the generation AI convert the voice data into text data.

[0031] The narrowing down unit can analyze a customer's selection history and past purchase history using AI. The narrowing down unit can analyze a customer's selection history using, for example, a machine learning algorithm and suggest optimal products. For example, the narrowing down unit can analyze a customer's selection history using a machine learning algorithm and suggest optimal products. The narrowing down unit can also analyze a customer's past purchase history using deep learning technology and suggest related products. For example, the narrowing down unit can analyze a customer's past purchase history using deep learning technology and suggest related products. Furthermore, the narrowing down unit can set filtering conditions and search for products that match selected elements. For example, the narrowing down unit can set filtering conditions such as price range, brand, and function and narrow down products that match these conditions. This allows more appropriate products to be suggested by analyzing the customer's selection history and past purchase history. Some or all of the above-described processing in the narrowing down unit can be performed using, for example, AI, or without AI. For example, the narrowing down unit can input customer selection history data into a generation AI and cause the generation AI to analyze the selection history.

[0032] The output unit may have a function to output the results on paper and send them to a smartphone. The output unit may, for example, print the results on paper using a printer. For example, the output unit may print the results on paper using a printer. The output unit may also send the results to a smartphone via email or app notification. For example, the output unit may send the results to a smartphone via email or app notification. Furthermore, the output unit may have a cloud unit that uses a cloud-based database and output the results using data stored on the cloud. For example, the output unit may have a cloud unit that uses a cloud-based database and output the results using data stored on the cloud. This allows the customer to not only receive the results on paper but also send them directly to their smartphone. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without AI. For example, the output unit may input result data to a generation AI and cause the generation AI to output the results.

[0033] The calling unit may have a function for sending a notification to the salesperson's smartphone. The calling unit may send the notification to the salesperson using, for example, a push notification or an SMS notification. For example, the calling unit may send the notification to the salesperson using a push notification or an SMS notification. The calling unit may also send an app notification to the salesperson's smartphone. For example, the calling unit may send an app notification to the salesperson's smartphone. This allows the salesperson to quickly respond to the customer's call. Some or all of the above-described processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit may input notification data into a generation AI and have the generation AI send the notification.

[0034] The narrowing down unit may include an information providing unit that provides product specifications, reviews, and comparison information. The narrowing down unit, for example, provides product specifications. For example, the narrowing down unit may provide technical specifications and performance indicators of the product. The narrowing down unit may also provide product reviews. For example, the narrowing down unit may provide user reviews and expert reviews. The narrowing down unit may also provide product comparison information. For example, the narrowing down unit may provide comparisons with other companies' products and price comparisons. This allows customers to check detailed product information. Some or all of the above-described processing in the narrowing down unit may be performed using AI, for example, or may be performed without using AI. For example, the narrowing down unit may input product reviews and comparison information to the generation AI and cause the generation AI to provide the information.

[0035] The output unit may include a cloud unit that uses a cloud-based database. The output unit may include, for example, a cloud unit that uses a cloud-based database, and may output results using data stored on the cloud. For example, the output unit may output results using a cloud-based database. This makes it easier to manage data by using a cloud-based database. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit may input data on the cloud to a generation AI and have the generation AI manage the data.

[0036] The selection unit can analyze the customer's past selection history and suggest optimal options when making a selection. The selection unit can suggest related options based on, for example, product categories previously selected by the customer. The selection unit can also prioritize displaying specific brands or price ranges based on the customer's past selection history. The selection unit can also prioritize displaying specific brands or price ranges based on the customer's past selection history. The selection unit can also suggest optimal options by referring to reviews and ratings of products previously selected by the customer. In this way, optimal options can be suggested by analyzing the customer's past selection history. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit can input customer selection history data into a generation AI and have the generation AI analyze the selection history.

[0037] At the time of selection, the selection unit can filter options based on the customer's current purchasing intent. For example, if the customer indicates a high purchasing intent, the selection unit can prioritize displaying products in a high price range. For example, if the customer indicates a high purchasing intent, the selection unit can prioritize displaying products in a high price range. Furthermore, if the customer indicates a low purchasing intent, the selection unit can prioritize displaying discounted items or sale items. For example, if the customer indicates a low purchasing intent, the selection unit can prioritize displaying discounted items or sale items. Furthermore, the selection unit can filter and display products with specific functions or features according to the customer's purchasing intent. For example, the selection unit can filter and display products with specific functions or features according to the customer's purchasing intent. This makes it possible to provide more appropriate options by filtering options according to the customer's purchasing intent. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input customer purchasing intent data into a generation AI and cause the generation AI to evaluate purchasing intent.

[0038] When making a selection, the selection unit can prioritize displaying highly relevant options by taking into account the customer's geographical location information. For example, if the customer is in a specific area, the selection unit can prioritize displaying products that are popular in that area. For example, if the customer is in a specific area, the selection unit can prioritize displaying products that are popular in that area. The selection unit can also prioritize displaying products that are sold in stores close to the customer's current location. For example, the selection unit can prioritize displaying products that are sold in stores close to the customer's current location. Furthermore, the selection unit can display information about promotions and sales that are limited to a specific area based on the customer's geographical location information. For example, the selection unit can display information about promotions and sales that are limited to a specific area based on the customer's geographical location information. This makes it possible to provide highly relevant options by taking the customer's geographical location information into consideration. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit may input the customer's geographical location data into a generation AI and cause the generation AI to analyze the location information.

[0039] The selection unit can analyze the customer's social media activity at the time of selection and suggest relevant options. For example, the selection unit can suggest relevant options based on products that the customer has "liked" or shared on social media. For example, the selection unit can suggest relevant options based on products that the customer has "liked" or shared on social media. The selection unit can also suggest options based on products purchased by the customer's followers or friends. For example, the selection unit can suggest options based on products purchased by the customer's followers or friends. Furthermore, the selection unit can suggest options based on topics or brands in which the customer has shown interest on social media. For example, the selection unit can suggest options based on topics or brands in which the customer has shown interest on social media. In this way, relevant options can be suggested by analyzing the customer's social media activity. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit can input the customer's social media data into a generation AI and cause the generation AI to analyze the social media activity.

[0040] When narrowing down the search results, the narrowing down unit can analyze the customer's past purchase history to suggest optimal products. The narrowing down unit can, for example, suggest products similar to products the customer has purchased in the past. For example, the narrowing down unit can suggest products similar to products the customer has purchased in the past. The narrowing down unit can also prioritize suggesting products of a specific brand or price range based on the customer's past purchase history. For example, the narrowing down unit can prioritize suggesting products of a specific brand or price range based on the customer's past purchase history. The narrowing down unit can also suggest related accessories or additional products based on the customer's purchase history. For example, the narrowing down unit can suggest related accessories or additional products based on the customer's purchase history. In this way, optimal products can be suggested by analyzing the customer's past purchase history. Some or all of the above-described processing in the narrowing down unit may be performed using, for example, AI, or may be performed without using AI. For example, the narrowing down unit can input the customer's purchase history data into the generation AI and cause the generation AI to analyze the purchase history.

[0041] The narrowing down unit can filter products based on the customer's current purchasing intent when narrowing down the results. For example, if the customer has a high purchasing intent, the narrowing down unit can prioritize displaying products in a high price range. For example, if the customer has a high purchasing intent, the narrowing down unit can prioritize displaying products in a high price range. Furthermore, if the customer has a low purchasing intent, the narrowing down unit can prioritize displaying discounted products or sale items. For example, if the customer has a low purchasing intent, the narrowing down unit can prioritize displaying discounted products or sale items. Furthermore, the narrowing down unit can filter and display products with specific functions or features according to the customer's purchasing intent. For example, the narrowing down unit can filter and display products with specific functions or features according to the customer's purchasing intent. This makes it possible to suggest more appropriate products by filtering products according to the customer's purchasing intent. Some or all of the above-described processing in the narrowing down unit may be performed using, for example, AI, or may be performed without using AI. For example, the narrowing down unit can input customer purchasing intent data into a generation AI and cause the generation AI to evaluate purchasing intent.

[0042] The narrowing down unit can suggest optimal products by taking into account the geographical distribution of the products when narrowing down the search results. For example, the narrowing down unit can prioritize suggesting products that are sold in stores close to the customer's current location. For example, the narrowing down unit can prioritize suggesting products that are sold in stores close to the customer's current location. The narrowing down unit can also prioritize suggesting products that are popular in a specific region. For example, the narrowing down unit can prioritize suggesting products that are popular in a specific region. Furthermore, the narrowing down unit can also suggest products that include regional promotions or sales information based on the geographical distribution. For example, the narrowing down unit can suggest products that include regional promotions or sales information based on the geographical distribution. This makes it possible to suggest optimal products by taking the geographical distribution of the products into consideration. Some or all of the above-described processing in the narrowing down unit may be performed using AI, for example, or may be performed without using AI. For example, the narrowing down unit can input geographical distribution data of the products to the generation AI and cause the generation AI to analyze the geographical distribution.

[0043] The narrowing down unit can improve the accuracy of the narrowing down by referring to literature related to the product during narrowing down. The narrowing down unit can suggest optimal products based on, for example, product reviews and ratings. For example, the narrowing down unit can suggest optimal products based on product reviews and ratings. The narrowing down unit can also provide detailed information by referring to technical literature and spec sheets of the product. For example, the narrowing down unit can provide detailed information by referring to technical literature and spec sheets of the product. The narrowing down unit can also suggest products that best meet the customer's needs based on literature related to the product. For example, the narrowing down unit can suggest products that best meet the customer's needs based on literature related to the product. As a result, by referring to literature related to the product, the accuracy of the narrowing down is improved. Some or all of the above-mentioned processing in the narrowing down unit may be performed using AI, for example, or may be performed without using AI. For example, the narrowing down unit can input literature data related to the product into the generation AI and cause the generation AI to analyze the related literature.

[0044] At the time of output, the output unit can analyze the customer's past output history and select the optimal output method. For example, the output unit can prioritize providing the output format (print, smartphone transmission, etc.) that the customer has used in the past. For example, the output unit can prioritize providing the output format (print, smartphone transmission, etc.) that the customer has used in the past. The output unit can also prioritize displaying specific information from the customer's past output history. For example, the output unit can prioritize displaying specific information from the customer's past output history. Furthermore, the output unit can suggest the optimal output method based on the customer's output history. For example, the output unit can suggest the optimal output method based on the customer's output history. In this way, the optimal output method can be provided by analyzing the customer's past output history. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the customer's output history data to a generation AI and have the generation AI analyze the output history.

[0045] The output unit can customize the output content based on the customer's current purchasing willingness at the time of output. For example, if the customer indicates a high purchasing willingness, the output unit can provide output content including detailed product information. For example, if the customer indicates a high purchasing willingness, the output unit can provide output content including detailed product information. Furthermore, if the customer indicates a low purchasing willingness, the output unit can provide output content including discount information or sale information. For example, if the customer indicates a low purchasing willingness, the output unit can provide output content including discount information or sale information. Furthermore, the output unit can provide output content that emphasizes specific functions or features depending on the customer's purchasing willingness. For example, the output unit can provide output content that emphasizes specific functions or features depending on the customer's purchasing willingness. This allows the output content to be customized according to the customer's purchasing willingness, thereby providing more appropriate information. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without AI. For example, the output unit can input the customer's purchasing willingness data into a generation AI and cause the generation AI to evaluate the purchasing willingness.

[0046] The output unit can select the optimal output method by taking into account the customer's geographical location information when outputting. For example, if the customer is in a store, the output unit can prioritize paper output. For example, if the customer is in a store, the output unit can prioritize paper output. The output unit can also prioritize sending to a smartphone when the customer is on the move. For example, if the customer is on the move, the output unit can prioritize sending to a smartphone. Furthermore, the output unit can suggest the optimal output method based on the customer's geographical location information. For example, the output unit can suggest the optimal output method based on the customer's geographical location information. This makes it possible to provide the optimal output method by taking the customer's geographical location information into consideration. Some or all of the above-described processing in the output unit can be performed using, or without, AI. For example, the output unit can input the customer's geographical location data to the generation AI and cause the generation AI to analyze the location information.

[0047] At the time of output, the output unit can analyze the customer's social media activity and suggest output content. The output unit can output, for example, information related to products that the customer has "liked" or shared on social media. For example, the output unit can output information related to products that the customer has "liked" or shared on social media. The output unit can also output information related to products purchased by the customer's followers or friends. For example, the output unit can output information related to products purchased by the customer's followers or friends. Furthermore, the output unit can output information related to topics or brands in which the customer has shown interest on social media. For example, the output unit can output information related to topics or brands in which the customer has shown interest on social media. This makes it possible to provide related information by analyzing the customer's social media activity. Some or all of the above-described processing in the output unit can be performed using, for example, AI, or without AI. For example, the output unit can input the customer's social media data to a generation AI and cause the generation AI to analyze the social media activity.

[0048] When making a call, the calling unit can analyze the customer's past call history and select the optimal calling method. For example, the calling unit can prioritize the calling method (button, voice, etc.) that the customer has used in the past. For example, the calling unit can prioritize the calling method (button, voice, etc.) that the customer has used in the past. The calling unit can also prioritize calls at specific times based on the customer's past call history. For example, the calling unit can prioritize calls at specific times based on the customer's past call history. Furthermore, the calling unit can suggest the optimal calling method based on the customer's call history. For example, the calling unit can suggest the optimal calling method based on the customer's call history. In this way, the optimal calling method can be provided by analyzing the customer's past call history. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the customer's call history data into the generation AI and have the generation AI analyze the call history.

[0049] When making a call, the calling unit can determine the call priority based on the customer's current purchasing intent. For example, if the customer indicates a high purchasing intent, the calling unit can immediately call a salesperson. For example, if the customer indicates a high purchasing intent, the calling unit can immediately call a salesperson. Furthermore, if the customer indicates a low purchasing intent, the calling unit can call a salesperson at an appropriate time. For example, if the customer indicates a low purchasing intent, the calling unit can call a salesperson at an appropriate time. Furthermore, the calling unit can preferentially call a specific salesperson depending on the customer's purchasing intent. For example, the calling unit can preferentially call a specific salesperson depending on the customer's purchasing intent. In this way, by determining the call priority based on the customer's purchasing intent, a salesperson can be called at a more appropriate time. Some or all of the above-described processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the customer's purchasing intent data into the generation AI and cause the generation AI to evaluate the purchasing intent.

[0050] When making a call, the calling unit can select the optimal calling method by taking into account the customer's geographical location information. For example, when a customer is in a store, the calling unit can prioritize calling a nearby salesperson. For example, when a customer is in a store, the calling unit can prioritize calling a nearby salesperson. Furthermore, when a customer is in a specific area, the calling unit can also call a salesperson corresponding to that area. For example, when a customer is in a specific area, the calling unit can call a salesperson corresponding to that area. Furthermore, the calling unit can suggest the optimal calling method based on the customer's geographical location information. In this way, the optimal calling method can be provided by taking the customer's geographical location information into consideration. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the customer's geographical location data into the generation AI and cause the generation AI to analyze the location information.

[0051] At the time of the call, the calling unit can analyze the customer's social media activity and suggest a call method. The calling unit, for example, calls a salesperson related to a product that the customer has "liked" or shared on social media. For example, the calling unit can call a salesperson related to a product that the customer has "liked" or shared on social media. The calling unit can also call a salesperson related to a product purchased by the customer's followers or friends. For example, the calling unit can call a salesperson related to a product purchased by the customer's followers or friends. The calling unit can also call a salesperson related to a topic or brand in which the customer has shown interest on social media. For example, the calling unit can call a salesperson related to a topic or brand in which the customer has shown interest on social media. In this way, a relevant salesperson can be called by analyzing the customer's social media activity. Some or all of the above-described processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the customer's social media data into the generation AI and cause the generation AI to analyze the social media activity.

[0052] When providing information, the information providing unit can analyze the customer's past information provision history and provide optimal information. The information providing unit can provide related information based on, for example, product information previously viewed by the customer. For example, the information providing unit can provide related information based on product information previously viewed by the customer. The information providing unit can also prioritize providing information on a specific brand or category based on the customer's past information provision history. For example, the information providing unit can prioritize providing information on a specific brand or category based on the customer's past information provision history. Furthermore, the information providing unit can suggest optimal information based on the customer's information provision history. For example, the information providing unit can suggest optimal information based on the customer's information provision history. In this way, optimal information can be provided by analyzing the customer's past information provision history. Some or all of the above-described processing in the information providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the information providing unit can input the customer's information provision history data to the generation AI and cause the generation AI to analyze the information provision history.

[0053] When providing information, the information providing unit can determine the priority of information provision based on the customer's current purchasing intent. For example, if the customer indicates a high purchasing intent, the information providing unit can prioritize providing detailed product information. For example, if the customer indicates a high purchasing intent, the information providing unit can prioritize providing detailed product information. Furthermore, if the customer indicates a low purchasing intent, the information providing unit can prioritize providing discount information or sale information. For example, if the customer indicates a low purchasing intent, the information providing unit can prioritize providing discount information or sale information. Furthermore, the information providing unit can provide information that emphasizes specific functions or features according to the customer's purchasing intent. For example, the information providing unit can provide information that emphasizes specific functions or features according to the customer's purchasing intent. This allows more appropriate information to be provided by determining the priority of information provision according to the customer's purchasing intent. Some or all of the above-described processing in the information providing unit may be performed using, or without, AI. For example, the information providing unit can input customer purchasing intent data into a generation AI and cause the generation AI to evaluate purchasing intent.

[0054] When providing information, the information providing unit can provide optimal information by taking into account the customer's geographical location information. For example, when a customer is in a store, the information providing unit can provide information about products sold in the store. For example, when a customer is in a store, the information providing unit can provide information about products sold in the store. Furthermore, when a customer is in a specific area, the information providing unit can provide information related to the area. For example, when a customer is in a specific area, the information providing unit can provide information related to the area. Furthermore, the information providing unit can provide area-specific promotions and sales information based on the customer's geographical location information. For example, the information providing unit can provide area-specific promotions and sales information based on the customer's geographical location information. This makes it possible to provide optimal information by taking into account the customer's geographical location information. Some or all of the above-described processing in the information providing unit may be performed using AI, or may be performed without using AI. For example, the information providing unit can input the customer's geographical location data into the generation AI and cause the generation AI to analyze the location information.

[0055] When providing information, the information providing unit can analyze the customer's social media activity and suggest content of the information to be provided. The information providing unit can, for example, provide information related to products that the customer has "liked" or shared on social media. For example, the information providing unit can provide information related to products that the customer has "liked" or shared on social media. The information providing unit can also provide information related to products purchased by the customer's followers or friends. For example, the information providing unit can provide information related to products purchased by the customer's followers or friends. Furthermore, the information providing unit can also provide information related to topics or brands in which the customer has shown interest on social media. For example, the information providing unit can provide information related to topics or brands in which the customer has shown interest on social media. This makes it possible to provide related information by analyzing the customer's social media activity. Some or all of the above-described processing in the information providing unit can be performed using, or without, AI. For example, the information providing unit can input the customer's social media data into the generation AI and cause the generation AI to analyze the social media activity.

[0056] When using cloud data, the cloud unit can select optimal data by referring to past data usage history. For example, the cloud unit can prioritize selecting related data based on data used by the customer in the past. For example, the cloud unit can prioritize selecting related data based on data used by the customer in the past. The cloud unit can also prioritize selecting data of a specific category or format based on the customer's past data usage history. For example, the cloud unit can prioritize selecting data of a specific category or format based on the customer's past data usage history. Furthermore, the cloud unit can suggest optimal data based on the customer's data usage history. This allows optimal data to be provided by referring to the past data usage history. Some or all of the above-described processing in the cloud unit may be performed using, for example, AI, or may be performed without AI. For example, the cloud unit can input the customer's data usage history into a generation AI and have the generation AI analyze the data usage history.

[0057] When using cloud data, the cloud unit can weight the data based on the time of data submission. For example, the cloud unit can prioritize use of the most recent data. For example, the cloud unit can prioritize use of the most recent data. The cloud unit can also weight the data by referring to past data. For example, the cloud unit can weight the data by referring to past data. Furthermore, the cloud unit can select optimal data based on the time of data submission. For example, the cloud unit can select optimal data based on the time of data submission. As a result, more appropriate data can be provided by weighting the data based on the time of data submission. Some or all of the above-mentioned processing in the cloud unit may be performed using AI, for example, or may be performed without using AI. For example, the cloud unit can input data on the time of data submission to a generation AI and have the generation AI analyze the submission time.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The selection unit can estimate the customer's purchasing intent and adjust the display order of options based on the estimated purchasing intent. For example, if the customer has a high purchasing intent, the selection unit can prioritize displaying products in a high price range. Alternatively, if the customer has a low purchasing intent, the selection unit can prioritize displaying discounted or sale items. Furthermore, the selection unit can filter and display products with specific functions or features according to the customer's purchasing intent. This makes it possible to provide more appropriate options by filtering options according to the customer's purchasing intent.

[0060] The selection unit can prioritize displaying highly relevant options by taking into account the customer's geographical location information. For example, if the customer is in a specific area, it can prioritize displaying products that are popular in that area. It can also prioritize displaying products that are sold in stores close to the customer's current location. Furthermore, it can display information about promotions and sales that are only available in that area based on the customer's geographical location information. In this way, it is possible to provide highly relevant options by taking into account the customer's geographical location information.

[0061] The narrowing down unit may include an information providing unit that provides product specifications, reviews, and comparison information. For example, the unit may provide technical specifications and performance indicators of the product. It may also provide user reviews and expert reviews. It may also provide comparisons with other companies' products and price comparisons. This allows the customer to check detailed product information.

[0062] At the time of output, the output unit can analyze the customer's past output history and select the optimal output method. For example, it can prioritize the output format (paper, smartphone transmission, etc.) that the customer has used in the past. It can also prioritize the display of specific information from the customer's past output history. Furthermore, it can also suggest the optimal output method based on the customer's output history. This makes it possible to provide the optimal output method by analyzing the customer's past output history.

[0063] When making a call, the calling unit can analyze the customer's past call history and select the optimal calling method. For example, it can prioritize calling methods (button, voice, etc.) that the customer has used in the past. It can also prioritize calls at specific times based on the customer's past call history. It can also suggest the optimal calling method based on the customer's call history. This makes it possible to provide the optimal calling method by analyzing the customer's past call history.

[0064] When providing information, the information provision department can analyze the customer's social media activity and suggest the content of the information to be provided. For example, it can provide information related to products that the customer has "liked" or shared on social media. It can also provide information related to products that the customer's followers or friends have purchased. It can also provide information related to topics or brands that the customer has shown interest in on social media. In this way, it is possible to provide relevant information by analyzing the customer's social media activity.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The selection unit selects customer preferences or required elements. Customer preferences or required elements include, for example, color, size, functionality, and price range. The selection unit provides an interface such as a touch panel, voice input, or two-dimensional code scanning. For example, a capacitive touch panel is used, allowing the customer to make a selection by touching the screen. Voice recognition technology can also be used to accept voice input from the customer. Furthermore, two-dimensional code scanning can be used, allowing the customer to make a selection by scanning a two-dimensional code with a smartphone. Step 2: The filter unit narrows down the products based on the elements selected by the selection unit. The filter unit uses AI to analyze the customer's selection history and past purchase history. For example, it uses a machine learning algorithm to analyze the customer's selection history and suggest the most suitable products. It can also use deep learning technology to analyze the customer's past purchase history and suggest related products. Furthermore, it is possible to set filtering conditions and search for products that match the selected elements. For example, filtering conditions such as price range, brand, and features can be set, and products that match these conditions are narrowed down. Step 3: The output unit outputs the results narrowed down by the narrowing unit. The output unit not only outputs the results on paper, but also has the function of sending them directly to a smartphone. For example, the results can be printed on paper using a printer. The results can also be sent to a smartphone via email or app notification. Furthermore, the system is equipped with a cloud unit that uses a cloud-based database, and can output the results using data stored on the cloud. Step 4: The calling unit calls the salesperson based on the results output by the output unit. The calling unit has a function to send a notification to the salesperson's smartphone. For example, the calling unit sends a notification to the salesperson using a push notification or SMS notification. It can also send an app notification to the salesperson's smartphone.

[0067] (Example 2) A sales support system according to an embodiment of the present invention uses a kiosk terminal installed in a store. A customer selects their preferences and necessary factors, prints the results on paper, etc., and then calls a salesperson to negotiate. This sales support system begins when a customer accesses a kiosk terminal and selects their preferences and necessary factors. Based on the selected factors, the kiosk terminal then narrows down the search to suitable products and prints the results on paper, etc. Based on the printed results, the customer can call a salesperson as needed to negotiate. This system allows customers to easily narrow down their search to the products they need, thereby reducing negotiation time for the store and increasing sales opportunities. For example, a customer accesses a kiosk terminal and selects their preferences and necessary factors. The kiosk terminal provides an interface, such as a touch panel or voice input, to facilitate customer operation. For example, the customer can select factors such as "price range," "brand," and "function." Based on the selected factors, the kiosk terminal then narrows down the search to suitable products. The kiosk terminal then accesses an internal database to search for products matching the selected factors. For example, if a customer selects "price range: ¥10,000 to ¥20,000," "brand: company A," and "function: waterproof," the system narrows down the search to products that match these criteria. The narrowed-down product results are printed out on paper or other media. Based on the results, customers can call a salesperson in the store as needed. For example, a kiosk terminal may have a "call salesperson" button, which can be pressed to call a salesperson. This system allows customers to easily narrow down the search to the products they need. For example, if a customer is looking for a product that meets specific criteria, they can use the kiosk terminal to quickly find the right product. It also reduces sales negotiation time and increases sales opportunities for stores. For example, salespeople no longer need to spend a long time with one customer, allowing them to attend to other customers. This allows the sales support system to allow customers to select their preferences and needs and narrow down the search to the right product.

[0068] A sales support system according to an embodiment includes a selection unit, a narrowing-down unit, an output unit, and a calling unit. The selection unit selects customer preferences or necessary factors. Examples of customer preferences or necessary factors include, but are not limited to, color, size, functionality, and price range. The selection unit provides an interface, such as a touch panel, voice input, or two-dimensional code (e.g., QR code) scanning. For example, the selection unit uses a capacitive touch panel to allow the customer to make a selection by touching the screen. The selection unit can also accept voice input from the customer using voice recognition technology. Furthermore, the selection unit can also use two-dimensional code scanning to allow the customer to make a selection by scanning a two-dimensional code with a smartphone. The narrowing-down unit narrows down the products based on the factors selected by the selection unit. For example, the narrowing-down unit uses AI to analyze the customer's selection history and past purchase history. For example, the narrowing-down unit uses a machine learning algorithm to analyze the customer's selection history and suggest optimal products. The narrowing-down unit can also use deep learning technology to analyze the customer's past purchase history and suggest related products. Furthermore, the narrowing-down unit can set filtering conditions and search for products that match the selected elements. For example, the narrowing-down unit sets filtering conditions such as price range, brand, and function, and narrows down the products that match these conditions. The output unit outputs the results narrowed down by the narrowing-down unit. For example, the output unit has a function not only to output the results on paper but also to send them directly to a smartphone. For example, the output unit prints the results on paper using a printer. The output unit can also send the results to the smartphone by email or app notification. Furthermore, the output unit has a cloud unit that uses a cloud-based database and can output the results using data stored on the cloud. The calling unit calls a salesperson based on the results output by the output unit. For example, the calling unit has a function to send a notification to the salesperson's smartphone. For example, the calling unit sends the notification to the salesperson using a push notification or SMS notification. The calling unit can also send an app notification to the salesperson's smartphone.As a result, the sales support system according to the embodiment allows customers to select their preferences and necessary factors and narrow down the search to find suitable products. For example, if a customer is looking for a product that meets specific criteria, the sales support system can be used to find the appropriate product in a short amount of time. Furthermore, stores can shorten negotiation time and increase sales opportunities. For example, salespeople no longer need to spend a long time with one customer, and can instead attend to other customers.

[0069] The selection unit can provide an interface for a touch panel, voice input, or two-dimensional code scanning. The selection unit can use, for example, a capacitive touch panel to allow a customer to make a selection by touching the screen. For example, the selection unit can use a capacitive touch panel to allow a customer to make a selection by touching the screen. The selection unit can also use voice recognition technology to accept a customer's voice input. For example, the selection unit can use voice recognition technology to accept a customer's voice input. The selection unit can also use two-dimensional code scanning to allow a customer to make a selection by scanning a two-dimensional code with a smartphone. For example, the selection unit can use two-dimensional code scanning to allow a customer to make a selection by scanning a two-dimensional code with a smartphone. This makes it possible to provide an interface that is easy for customers to operate. Some or all of the above-described processing in the selection unit can be performed using, for example, AI, or without AI. For example, the selection unit can input a customer's voice input to a generation AI and have the generation AI convert the voice data into text data.

[0070] The narrowing down unit can analyze a customer's selection history and past purchase history using AI. The narrowing down unit can analyze a customer's selection history using, for example, a machine learning algorithm and suggest optimal products. For example, the narrowing down unit can analyze a customer's selection history using a machine learning algorithm and suggest optimal products. The narrowing down unit can also analyze a customer's past purchase history using deep learning technology and suggest related products. For example, the narrowing down unit can analyze a customer's past purchase history using deep learning technology and suggest related products. Furthermore, the narrowing down unit can set filtering conditions and search for products that match selected elements. For example, the narrowing down unit can set filtering conditions such as price range, brand, and function and narrow down products that match these conditions. This allows more appropriate products to be suggested by analyzing the customer's selection history and past purchase history. Some or all of the above-described processing in the narrowing down unit can be performed using, for example, AI, or without AI. For example, the narrowing down unit can input customer selection history data into a generation AI and cause the generation AI to analyze the selection history.

[0071] The output unit may have a function to output the results on paper and send them to a smartphone. The output unit may, for example, print the results on paper using a printer. For example, the output unit may print the results on paper using a printer. The output unit may also send the results to a smartphone via email or app notification. For example, the output unit may send the results to a smartphone via email or app notification. Furthermore, the output unit may have a cloud unit that uses a cloud-based database and output the results using data stored on the cloud. For example, the output unit may have a cloud unit that uses a cloud-based database and output the results using data stored on the cloud. This allows the customer to not only receive the results on paper but also send them directly to their smartphone. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without AI. For example, the output unit may input result data to a generation AI and cause the generation AI to output the results.

[0072] The calling unit may have a function for sending a notification to the salesperson's smartphone. The calling unit may send the notification to the salesperson using, for example, a push notification or an SMS notification. For example, the calling unit may send the notification to the salesperson using a push notification or an SMS notification. The calling unit may also send an app notification to the salesperson's smartphone. For example, the calling unit may send an app notification to the salesperson's smartphone. This allows the salesperson to quickly respond to the customer's call. Some or all of the above-described processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit may input notification data into a generation AI and have the generation AI send the notification.

[0073] The narrowing down unit may include an information providing unit that provides product specifications, reviews, and comparison information. The narrowing down unit, for example, provides product specifications. For example, the narrowing down unit may provide technical specifications and performance indicators of the product. The narrowing down unit may also provide product reviews. For example, the narrowing down unit may provide user reviews and expert reviews. The narrowing down unit may also provide product comparison information. For example, the narrowing down unit may provide comparisons with other companies' products and price comparisons. This allows customers to check detailed product information. Some or all of the above-described processing in the narrowing down unit may be performed using AI, for example, or may be performed without using AI. For example, the narrowing down unit may input product reviews and comparison information to the generation AI and cause the generation AI to provide the information.

[0074] The output unit may include a cloud unit that uses a cloud-based database. The output unit may include, for example, a cloud unit that uses a cloud-based database, and may output results using data stored on the cloud. For example, the output unit may output results using a cloud-based database. This makes it easier to manage data by using a cloud-based database. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit may input data on the cloud to a generation AI and have the generation AI manage the data.

[0075] The selection unit can estimate the customer's emotions and adjust the display order of options based on the estimated customer emotions. For example, if the customer is feeling stressed, the selection unit can prioritize displaying simple and intuitive options. For example, if the customer is feeling stressed, the selection unit can prioritize displaying simple and intuitive options. Furthermore, if the customer is relaxed, the selection unit can display detailed options and provide customizable options. For example, if the customer is relaxed, the selection unit can display detailed options and provide customizable options. Furthermore, if the customer is in a hurry, the selection unit can prioritize displaying the most popular options. For example, if the customer is in a hurry, the selection unit can prioritize displaying the most popular options. This allows the customer to be provided with more appropriate options by adjusting the display order of options according to the customer's emotions. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit may input customer emotion data into a generation AI and cause the generation AI to estimate the emotion.

[0076] The selection unit can analyze the customer's past selection history and suggest optimal options when making a selection. The selection unit can suggest related options based on, for example, product categories previously selected by the customer. The selection unit can also prioritize displaying specific brands or price ranges based on the customer's past selection history. The selection unit can also prioritize displaying specific brands or price ranges based on the customer's past selection history. The selection unit can also suggest optimal options by referring to reviews and ratings of products previously selected by the customer. In this way, optimal options can be suggested by analyzing the customer's past selection history. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit can input customer selection history data into a generation AI and have the generation AI analyze the selection history.

[0077] At the time of selection, the selection unit can filter options based on the customer's current purchasing intent. For example, if the customer indicates a high purchasing intent, the selection unit can prioritize displaying products in a high price range. For example, if the customer indicates a high purchasing intent, the selection unit can prioritize displaying products in a high price range. Furthermore, if the customer indicates a low purchasing intent, the selection unit can prioritize displaying discounted items or sale items. For example, if the customer indicates a low purchasing intent, the selection unit can prioritize displaying discounted items or sale items. Furthermore, the selection unit can filter and display products with specific functions or features according to the customer's purchasing intent. For example, the selection unit can filter and display products with specific functions or features according to the customer's purchasing intent. This makes it possible to provide more appropriate options by filtering options according to the customer's purchasing intent. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input customer purchasing intent data into a generation AI and cause the generation AI to evaluate purchasing intent.

[0078] The selection unit can estimate the customer's emotions and adjust the number of options based on the estimated customer emotions. For example, if the customer is feeling stressed, the selection unit can reduce the number of options and provide simpler options. For example, if the customer is feeling stressed, the selection unit can reduce the number of options and provide simpler options. The selection unit can also increase the number of options and provide more detailed options if the customer is relaxed. For example, if the customer is relaxed, the selection unit can increase the number of options and provide more detailed options. Furthermore, if the customer is in a hurry, the selection unit can display fewer of the most popular options. For example, if the customer is in a hurry, the selection unit can display fewer of the most popular options. This allows more appropriate options to be provided by adjusting the number of options according to the customer's emotions. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit may input customer emotion data into a generation AI and cause the generation AI to estimate the emotion.

[0079] When making a selection, the selection unit can prioritize displaying highly relevant options by taking into account the customer's geographical location information. For example, if the customer is in a specific area, the selection unit can prioritize displaying products that are popular in that area. For example, if the customer is in a specific area, the selection unit can prioritize displaying products that are popular in that area. The selection unit can also prioritize displaying products that are sold in stores close to the customer's current location. For example, the selection unit can prioritize displaying products that are sold in stores close to the customer's current location. Furthermore, the selection unit can display information about promotions and sales that are limited to a specific area based on the customer's geographical location information. For example, the selection unit can display information about promotions and sales that are limited to a specific area based on the customer's geographical location information. This makes it possible to provide highly relevant options by taking the customer's geographical location information into consideration. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit may input the customer's geographical location data into a generation AI and cause the generation AI to analyze the location information.

[0080] The selection unit can analyze the customer's social media activity at the time of selection and suggest relevant options. For example, the selection unit can suggest relevant options based on products that the customer has "liked" or shared on social media. For example, the selection unit can suggest relevant options based on products that the customer has "liked" or shared on social media. The selection unit can also suggest options based on products purchased by the customer's followers or friends. For example, the selection unit can suggest options based on products purchased by the customer's followers or friends. Furthermore, the selection unit can suggest options based on topics or brands in which the customer has shown interest on social media. For example, the selection unit can suggest options based on topics or brands in which the customer has shown interest on social media. In this way, relevant options can be suggested by analyzing the customer's social media activity. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit can input the customer's social media data into a generation AI and cause the generation AI to analyze the social media activity.

[0081] The narrowing down unit can estimate the customer's emotions and adjust the narrowing down criteria based on the estimated customer's emotions. For example, if the customer is feeling stressed, the narrowing down unit can provide simple narrowing down criteria. For example, if the customer is feeling stressed, the narrowing down unit can provide simple narrowing down criteria. Furthermore, if the customer is relaxed, the narrowing down unit can provide detailed narrowing down criteria. For example, if the customer is feeling relaxed, the narrowing down unit can provide detailed narrowing down criteria. Furthermore, if the customer is in a hurry, the narrowing down unit can display the most important criteria. For example, if the customer is in a hurry, the narrowing down unit can display the most important criteria. This allows the narrowing down of the narrowing down criteria according to the customer's emotions, thereby suggesting more appropriate products. Some or all of the above-described processing in the narrowing down unit can be performed using AI, for example, or without AI. For example, the narrowing down unit can input customer emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0082] When narrowing down the search results, the narrowing down unit can analyze the customer's past purchase history to suggest optimal products. The narrowing down unit can, for example, suggest products similar to products the customer has purchased in the past. For example, the narrowing down unit can suggest products similar to products the customer has purchased in the past. The narrowing down unit can also prioritize suggesting products of a specific brand or price range based on the customer's past purchase history. For example, the narrowing down unit can prioritize suggesting products of a specific brand or price range based on the customer's past purchase history. The narrowing down unit can also suggest related accessories or additional products based on the customer's purchase history. For example, the narrowing down unit can suggest related accessories or additional products based on the customer's purchase history. In this way, optimal products can be suggested by analyzing the customer's past purchase history. Some or all of the above-described processing in the narrowing down unit may be performed using, for example, AI, or may be performed without using AI. For example, the narrowing down unit can input the customer's purchase history data into the generation AI and cause the generation AI to analyze the purchase history.

[0083] The narrowing down unit can filter products based on the customer's current purchasing intent when narrowing down the results. For example, if the customer has a high purchasing intent, the narrowing down unit can prioritize displaying products in a high price range. For example, if the customer has a high purchasing intent, the narrowing down unit can prioritize displaying products in a high price range. Furthermore, if the customer has a low purchasing intent, the narrowing down unit can prioritize displaying discounted products or sale items. For example, if the customer has a low purchasing intent, the narrowing down unit can prioritize displaying discounted products or sale items. Furthermore, the narrowing down unit can filter and display products with specific functions or features according to the customer's purchasing intent. For example, the narrowing down unit can filter and display products with specific functions or features according to the customer's purchasing intent. This makes it possible to suggest more appropriate products by filtering products according to the customer's purchasing intent. Some or all of the above-described processing in the narrowing down unit may be performed using, for example, AI, or may be performed without using AI. For example, the narrowing down unit can input customer purchasing intent data into a generation AI and cause the generation AI to evaluate purchasing intent.

[0084] The narrowing-down unit can estimate the customer's emotions and adjust the order in which the narrowing-down results are displayed based on the estimated customer emotions. For example, if the customer is feeling stressed, the narrowing-down unit can display the results in a simple and intuitive order. For example, if the customer is feeling stressed, the narrowing-down unit can display the results in a simple and intuitive order. Furthermore, if the customer is relaxed, the narrowing-down unit can display the results in an order that includes detailed information. For example, if the customer is relaxed, the narrowing-down unit can display the results in an order that includes detailed information. Furthermore, if the customer is in a hurry, the narrowing-down unit can prioritize displaying the most popular products. For example, if the customer is in a hurry, the narrowing-down unit can prioritize displaying the most popular products. This allows more appropriate products to be suggested by adjusting the order in which the narrowing-down results are displayed based on the customer's emotions. Some or all of the above-described processing in the narrowing-down unit may be performed using AI, for example, or may be performed without using AI. For example, the narrowing-down unit can input customer emotion data into a generation AI and cause the generation AI to estimate the emotion.

[0085] The narrowing down unit can suggest optimal products by taking into account the geographical distribution of the products when narrowing down the search results. For example, the narrowing down unit can prioritize suggesting products that are sold in stores close to the customer's current location. For example, the narrowing down unit can prioritize suggesting products that are sold in stores close to the customer's current location. The narrowing down unit can also prioritize suggesting products that are popular in a specific region. For example, the narrowing down unit can prioritize suggesting products that are popular in a specific region. Furthermore, the narrowing down unit can also suggest products that include regional promotions or sales information based on the geographical distribution. For example, the narrowing down unit can suggest products that include regional promotions or sales information based on the geographical distribution. This makes it possible to suggest optimal products by taking the geographical distribution of the products into consideration. Some or all of the above-described processing in the narrowing down unit may be performed using AI, for example, or may be performed without using AI. For example, the narrowing down unit can input geographical distribution data of the products to the generation AI and cause the generation AI to analyze the geographical distribution.

[0086] The narrowing down unit can improve the accuracy of the narrowing down by referring to literature related to the product during narrowing down. The narrowing down unit can suggest optimal products based on, for example, product reviews and ratings. For example, the narrowing down unit can suggest optimal products based on product reviews and ratings. The narrowing down unit can also provide detailed information by referring to technical literature and spec sheets of the product. For example, the narrowing down unit can provide detailed information by referring to technical literature and spec sheets of the product. The narrowing down unit can also suggest products that best meet the customer's needs based on literature related to the product. For example, the narrowing down unit can suggest products that best meet the customer's needs based on literature related to the product. As a result, by referring to literature related to the product, the accuracy of the narrowing down is improved. Some or all of the above-mentioned processing in the narrowing down unit may be performed using AI, for example, or may be performed without using AI. For example, the narrowing down unit can input literature data related to the product into the generation AI and cause the generation AI to analyze the related literature.

[0087] The output unit can estimate the customer's emotions and adjust the output format based on the estimated customer emotions. For example, if the customer is feeling stressed, the output unit can output in a simple, highly visible format. For example, if the customer is feeling stressed, the output unit can output in a simple, highly visible format. Furthermore, if the customer is relaxed, the output unit can output in a format including detailed information. For example, if the customer is relaxed, the output unit can output in a format including detailed information. Furthermore, if the customer is in a hurry, the output unit can output in a concise format that focuses on the main points. For example, if the customer is in a hurry, the output unit can output in a concise format that focuses on the main points. This allows more appropriate information to be provided by adjusting the output format according to the customer's emotions. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input customer emotion data to a generation AI and cause the generation AI to estimate the emotion.

[0088] At the time of output, the output unit can analyze the customer's past output history and select the optimal output method. For example, the output unit can prioritize providing the output format (print, smartphone transmission, etc.) that the customer has used in the past. For example, the output unit can prioritize providing the output format (print, smartphone transmission, etc.) that the customer has used in the past. The output unit can also prioritize displaying specific information from the customer's past output history. For example, the output unit can prioritize displaying specific information from the customer's past output history. Furthermore, the output unit can suggest the optimal output method based on the customer's output history. For example, the output unit can suggest the optimal output method based on the customer's output history. In this way, the optimal output method can be provided by analyzing the customer's past output history. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the customer's output history data to a generation AI and have the generation AI analyze the output history.

[0089] The output unit can customize the output content based on the customer's current purchasing willingness at the time of output. For example, if the customer indicates a high purchasing willingness, the output unit can provide output content including detailed product information. For example, if the customer indicates a high purchasing willingness, the output unit can provide output content including detailed product information. Furthermore, if the customer indicates a low purchasing willingness, the output unit can provide output content including discount information or sale information. For example, if the customer indicates a low purchasing willingness, the output unit can provide output content including discount information or sale information. Furthermore, the output unit can provide output content that emphasizes specific functions or features depending on the customer's purchasing willingness. For example, the output unit can provide output content that emphasizes specific functions or features depending on the customer's purchasing willingness. This allows the output content to be customized according to the customer's purchasing willingness, thereby providing more appropriate information. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without AI. For example, the output unit can input the customer's purchasing willingness data into a generation AI and cause the generation AI to evaluate the purchasing willingness.

[0090] The output unit can estimate the customer's emotions and determine output priorities based on the estimated customer emotions. For example, when a customer is feeling stressed, the output unit can prioritize output of the most important information. For example, when a customer is feeling stressed, the output unit can prioritize output of the most important information. Furthermore, when a customer is relaxed, the output unit can provide output including detailed information. For example, when a customer is relaxed, the output unit can provide output including detailed information. Furthermore, when a customer is in a hurry, the output unit can provide concise output that focuses on the main points. For example, when a customer is in a hurry, the output unit can provide concise output that focuses on the main points. In this way, by determining output priorities according to the customer's emotions, more appropriate information can be provided. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input customer emotion data to a generation AI and cause the generation AI to estimate emotions.

[0091] The output unit can select the optimal output method by taking into account the customer's geographical location information when outputting. For example, if the customer is in a store, the output unit can prioritize paper output. For example, if the customer is in a store, the output unit can prioritize paper output. The output unit can also prioritize sending to a smartphone when the customer is on the move. For example, if the customer is on the move, the output unit can prioritize sending to a smartphone. Furthermore, the output unit can suggest the optimal output method based on the customer's geographical location information. For example, the output unit can suggest the optimal output method based on the customer's geographical location information. This makes it possible to provide the optimal output method by taking the customer's geographical location information into consideration. Some or all of the above-described processing in the output unit can be performed using, or without, AI. For example, the output unit can input the customer's geographical location data to the generation AI and cause the generation AI to analyze the location information.

[0092] At the time of output, the output unit can analyze the customer's social media activity and suggest output content. The output unit can output, for example, information related to products that the customer has "liked" or shared on social media. For example, the output unit can output information related to products that the customer has "liked" or shared on social media. The output unit can also output information related to products purchased by the customer's followers or friends. For example, the output unit can output information related to products purchased by the customer's followers or friends. Furthermore, the output unit can output information related to topics or brands in which the customer has shown interest on social media. For example, the output unit can output information related to topics or brands in which the customer has shown interest on social media. This makes it possible to provide related information by analyzing the customer's social media activity. Some or all of the above-described processing in the output unit can be performed using, for example, AI, or without AI. For example, the output unit can input the customer's social media data to a generation AI and cause the generation AI to analyze the social media activity.

[0093] The calling unit can estimate the customer's emotions and adjust the timing of the call based on the estimated customer's emotions. For example, if the customer is feeling stressed, the calling unit can quickly call a salesperson. For example, if the customer is feeling stressed, the calling unit can quickly call a salesperson. Furthermore, if the customer is relaxed, the calling unit can also call a salesperson at an appropriate time. For example, if the customer is relaxed, the calling unit can call a salesperson at an appropriate time. Furthermore, if the customer is in a hurry, the calling unit can also immediately call a salesperson. For example, if the customer is in a hurry, the calling unit can immediately call a salesperson. In this way, by adjusting the timing of the call according to the customer's emotions, a salesperson can be called at a more appropriate time. Some or all of the above-described processing in the calling unit may be performed using AI, for example, or may be performed without using AI. For example, the calling unit can input customer emotion data into a generation AI and cause the generation AI to estimate the emotion.

[0094] When making a call, the calling unit can analyze the customer's past call history and select the optimal calling method. For example, the calling unit can prioritize the calling method (button, voice, etc.) that the customer has used in the past. For example, the calling unit can prioritize the calling method (button, voice, etc.) that the customer has used in the past. The calling unit can also prioritize calls at specific times based on the customer's past call history. For example, the calling unit can prioritize calls at specific times based on the customer's past call history. Furthermore, the calling unit can suggest the optimal calling method based on the customer's call history. For example, the calling unit can suggest the optimal calling method based on the customer's call history. In this way, the optimal calling method can be provided by analyzing the customer's past call history. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the customer's call history data into the generation AI and have the generation AI analyze the call history.

[0095] When making a call, the calling unit can determine the call priority based on the customer's current purchasing intent. For example, if the customer indicates a high purchasing intent, the calling unit can immediately call a salesperson. For example, if the customer indicates a high purchasing intent, the calling unit can immediately call a salesperson. Furthermore, if the customer indicates a low purchasing intent, the calling unit can call a salesperson at an appropriate time. For example, if the customer indicates a low purchasing intent, the calling unit can call a salesperson at an appropriate time. Furthermore, the calling unit can preferentially call a specific salesperson depending on the customer's purchasing intent. For example, the calling unit can preferentially call a specific salesperson depending on the customer's purchasing intent. In this way, by determining the call priority based on the customer's purchasing intent, a salesperson can be called at a more appropriate time. Some or all of the above-described processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the customer's purchasing intent data into the generation AI and cause the generation AI to evaluate the purchasing intent.

[0096] The calling unit can estimate the customer's emotions and adjust the calling method based on the estimated customer emotions. For example, if the customer is feeling stressed, the calling unit can make a call with a simple button. For example, if the customer is feeling stressed, the calling unit can make a call with a simple button. Furthermore, if the customer is relaxed, the calling unit can make a call with voice input. For example, if the customer is relaxed, the calling unit can make a call with voice input. Furthermore, if the customer is in a hurry, the calling unit can provide a calling method that responds immediately. For example, if the customer is in a hurry, the calling unit can provide a calling method that responds immediately. This allows the salesperson to be called in a more appropriate manner by adjusting the calling method according to the customer's emotions. Some or all of the above-described processing in the calling unit may be performed using AI, or may be performed without using AI. For example, the calling unit can input customer emotion data into a generation AI and have the generation AI perform emotion estimation.

[0097] When making a call, the calling unit can select the optimal calling method by taking into account the customer's geographical location information. For example, when a customer is in a store, the calling unit can prioritize calling a nearby salesperson. For example, when a customer is in a store, the calling unit can prioritize calling a nearby salesperson. Furthermore, when a customer is in a specific area, the calling unit can also call a salesperson corresponding to that area. For example, when a customer is in a specific area, the calling unit can call a salesperson corresponding to that area. Furthermore, the calling unit can suggest the optimal calling method based on the customer's geographical location information. In this way, the optimal calling method can be provided by taking the customer's geographical location information into consideration. Some or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the customer's geographical location data into the generation AI and cause the generation AI to analyze the location information.

[0098] At the time of the call, the calling unit can analyze the customer's social media activity and suggest a call method. The calling unit, for example, calls a salesperson related to a product that the customer has "liked" or shared on social media. For example, the calling unit can call a salesperson related to a product that the customer has "liked" or shared on social media. The calling unit can also call a salesperson related to a product purchased by the customer's followers or friends. For example, the calling unit can call a salesperson related to a product purchased by the customer's followers or friends. The calling unit can also call a salesperson related to a topic or brand in which the customer has shown interest on social media. For example, the calling unit can call a salesperson related to a topic or brand in which the customer has shown interest on social media. In this way, a relevant salesperson can be called by analyzing the customer's social media activity. Some or all of the above-described processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can input the customer's social media data into the generation AI and cause the generation AI to analyze the social media activity.

[0099] The information providing unit can estimate the customer's emotions and adjust the content of the information provided based on the estimated customer's emotions. For example, if the customer is feeling stressed, the information providing unit can provide simple, highly visible information. For example, if the customer is feeling stressed, the information providing unit can provide simple, highly visible information. Furthermore, if the customer is relaxed, the information providing unit can provide detailed information. For example, if the customer is relaxed, the information providing unit can provide detailed information. Furthermore, if the customer is in a hurry, the information providing unit can provide concise information that focuses on the main points. For example, if the customer is in a hurry, the information providing unit can provide concise information that focuses on the main points. This allows the content of the information provided to be adjusted according to the customer's emotions, thereby providing more appropriate information. Some or all of the above-described processing in the information providing unit may be performed using AI, for example, or may be performed without using AI. For example, the information providing unit can input customer emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0100] When providing information, the information providing unit can analyze the customer's past information provision history and provide optimal information. The information providing unit can provide related information based on, for example, product information previously viewed by the customer. For example, the information providing unit can provide related information based on product information previously viewed by the customer. The information providing unit can also prioritize providing information on a specific brand or category based on the customer's past information provision history. For example, the information providing unit can prioritize providing information on a specific brand or category based on the customer's past information provision history. Furthermore, the information providing unit can suggest optimal information based on the customer's information provision history. For example, the information providing unit can suggest optimal information based on the customer's information provision history. In this way, optimal information can be provided by analyzing the customer's past information provision history. Some or all of the above-described processing in the information providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the information providing unit can input the customer's information provision history data to the generation AI and cause the generation AI to analyze the information provision history.

[0101] When providing information, the information providing unit can determine the priority of information provision based on the customer's current purchasing intent. For example, if the customer indicates a high purchasing intent, the information providing unit can prioritize providing detailed product information. For example, if the customer indicates a high purchasing intent, the information providing unit can prioritize providing detailed product information. Furthermore, if the customer indicates a low purchasing intent, the information providing unit can prioritize providing discount information or sale information. For example, if the customer indicates a low purchasing intent, the information providing unit can prioritize providing discount information or sale information. Furthermore, the information providing unit can provide information that emphasizes specific functions or features according to the customer's purchasing intent. For example, the information providing unit can provide information that emphasizes specific functions or features according to the customer's purchasing intent. This allows more appropriate information to be provided by determining the priority of information provision according to the customer's purchasing intent. Some or all of the above-described processing in the information providing unit may be performed using, or without, AI. For example, the information providing unit can input customer purchasing intent data into a generation AI and cause the generation AI to evaluate purchasing intent.

[0102] The information providing unit can estimate the customer's emotions and adjust the method of providing information based on the estimated customer's emotions. For example, if the customer is feeling stressed, the information providing unit can provide information using a simple interface. For example, if the customer is feeling stressed, the information providing unit can provide information using a simple interface. Furthermore, if the customer is relaxed, the information providing unit can provide information using a detailed interface. For example, if the customer is relaxed, the information providing unit can provide information using a detailed interface. Furthermore, if the customer is in a hurry, the information providing unit can select a method of quickly providing information. For example, if the customer is in a hurry, the information providing unit can select a method of quickly providing information. This allows more appropriate information to be provided by adjusting the method of providing information according to the customer's emotions. Some or all of the above-described processing in the information providing unit may be performed using AI, for example, or may be performed without using AI. For example, the information providing unit can input customer emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0103] When providing information, the information providing unit can provide optimal information by taking into account the customer's geographical location information. For example, when a customer is in a store, the information providing unit can provide information about products sold in the store. For example, when a customer is in a store, the information providing unit can provide information about products sold in the store. Furthermore, when a customer is in a specific area, the information providing unit can provide information related to the area. For example, when a customer is in a specific area, the information providing unit can provide information related to the area. Furthermore, the information providing unit can provide area-specific promotions and sales information based on the customer's geographical location information. For example, the information providing unit can provide area-specific promotions and sales information based on the customer's geographical location information. This makes it possible to provide optimal information by taking into account the customer's geographical location information. Some or all of the above-described processing in the information providing unit may be performed using AI, or may be performed without using AI. For example, the information providing unit can input the customer's geographical location data into the generation AI and cause the generation AI to analyze the location information.

[0104] When providing information, the information providing unit can analyze the customer's social media activity and suggest content of the information to be provided. The information providing unit can, for example, provide information related to products that the customer has "liked" or shared on social media. For example, the information providing unit can provide information related to products that the customer has "liked" or shared on social media. The information providing unit can also provide information related to products purchased by the customer's followers or friends. For example, the information providing unit can provide information related to products purchased by the customer's followers or friends. Furthermore, the information providing unit can also provide information related to topics or brands in which the customer has shown interest on social media. For example, the information providing unit can provide information related to topics or brands in which the customer has shown interest on social media. This makes it possible to provide related information by analyzing the customer's social media activity. Some or all of the above-described processing in the information providing unit can be performed using, or without, AI. For example, the information providing unit can input the customer's social media data into the generation AI and cause the generation AI to analyze the social media activity.

[0105] The cloud unit can estimate a customer's emotions and select cloud data based on the estimated customer emotions. For example, if a customer is feeling stressed, the cloud unit can prioritize selecting simple and intuitive data. For example, if a customer is feeling stressed, the cloud unit can prioritize selecting simple and intuitive data. Furthermore, if a customer is relaxed, the cloud unit can select detailed data. For example, if a customer is relaxed, the cloud unit can select detailed data. Furthermore, if a customer is in a hurry, the cloud unit can prioritize selecting data that can be accessed quickly. For example, if a customer is in a hurry, the cloud unit can prioritize selecting data that can be accessed quickly. This allows for more appropriate data to be provided by selecting cloud data according to the customer's emotions. Some or all of the above-described processing in the cloud unit may be performed using AI, for example, or without AI. For example, the cloud unit can input customer emotion data into a generation AI and have the generation AI perform emotion estimation.

[0106] When using cloud data, the cloud unit can select optimal data by referring to past data usage history. For example, the cloud unit can prioritize selecting related data based on data used by the customer in the past. For example, the cloud unit can prioritize selecting related data based on data used by the customer in the past. The cloud unit can also prioritize selecting data of a specific category or format based on the customer's past data usage history. For example, the cloud unit can prioritize selecting data of a specific category or format based on the customer's past data usage history. Furthermore, the cloud unit can suggest optimal data based on the customer's data usage history. This allows optimal data to be provided by referring to the past data usage history. Some or all of the above-described processing in the cloud unit may be performed using, for example, AI, or may be performed without AI. For example, the cloud unit can input the customer's data usage history into a generation AI and have the generation AI analyze the data usage history.

[0107] The cloud unit can estimate the customer's emotions and adjust the frequency of cloud data usage based on the estimated customer emotions. For example, when a customer is feeling stressed, the cloud unit can frequently use simple, intuitive data. For example, when a customer is feeling stressed, the cloud unit can frequently use simple, intuitive data. Furthermore, when a customer is relaxed, the cloud unit can frequently use detailed data. For example, when a customer is relaxed, the cloud unit can frequently use detailed data. Furthermore, when a customer is in a hurry, the cloud unit can frequently use quickly accessible data. For example, when a customer is in a hurry, the cloud unit can frequently use quickly accessible data. This allows for providing more appropriate data by adjusting the frequency of cloud data usage according to the customer's emotions. Some or all of the above-described processing in the cloud unit may be performed using AI, for example, or without AI. For example, the cloud unit can input customer emotion data into a generation AI and have the generation AI perform emotion estimation.

[0108] When using cloud data, the cloud unit can weight the data based on the time of data submission. For example, the cloud unit can prioritize use of the most recent data. For example, the cloud unit can prioritize use of the most recent data. The cloud unit can also weight the data by referring to past data. For example, the cloud unit can weight the data by referring to past data. Furthermore, the cloud unit can select optimal data based on the time of data submission. For example, the cloud unit can select optimal data based on the time of data submission. As a result, more appropriate data can be provided by weighting the data based on the time of data submission. Some or all of the above-mentioned processing in the cloud unit may be performed using AI, for example, or may be performed without using AI. For example, the cloud unit can input data on the time of data submission to a generation AI and have the generation AI analyze the submission time. === Hard Collateral 1-1 === Each of the multiple elements including the selection unit, narrowing-down unit, output unit, and calling unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit can select the customer's preferences or necessary elements using a touch panel or voice input interface of the smart device 14. The narrowing-down unit is realized by the specific processing unit 290 of the data processing device 12 and narrows down the products based on the selected elements. The output unit can output the results using a printer or email transmission function of the smart device 14. The calling unit can call a salesperson using a notification function of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the selection unit, narrowing down unit, output unit, and calling unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit can select the customer's preferences or necessary elements using a voice input interface of the smart glasses 214. The narrowing down unit is realized by the specific processing unit 290 of the data processing device 12 and narrows down the products based on the selected elements. The output unit can output the results using the display or email sending function of the smart glasses 214. The calling unit can call a salesperson using the notification function of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned selection unit, narrowing down unit, output unit, and calling unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the selection unit can select the customer's preferences or necessary elements using the voice input interface of the headset terminal 314. The narrowing down unit is realized by the specific processing unit 290 of the data processing device 12 and narrows down the products based on the selected elements. The output unit can output the results using the display or email sending function of the headset terminal 314. The calling unit can call a salesperson using the notification function of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned selection unit, narrowing-down unit, output unit, and calling unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit can select the customer's preferences or necessary elements using a voice input interface of the robot 414. The narrowing-down unit is realized by the specific processing unit 290 of the data processing device 12 and narrows down the products based on the selected elements. The output unit can output the results using the display or email sending function of the robot 414. The calling unit can call a salesperson using the notification function of the robot 414.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The selection unit can estimate the customer's purchasing intent and adjust the display order of options based on the estimated purchasing intent. For example, if the customer has a high purchasing intent, the selection unit can prioritize displaying products in a high price range. Alternatively, if the customer has a low purchasing intent, the selection unit can prioritize displaying discounted or sale items. Furthermore, the selection unit can filter and display products with specific functions or features according to the customer's purchasing intent. This makes it possible to provide more appropriate options by filtering options according to the customer's purchasing intent.

[0111] The filtering unit can estimate the customer's emotions and adjust the filtering criteria based on the estimated emotions. For example, if the customer is feeling stressed, the filtering unit can provide simple filtering criteria. If the customer is feeling relaxed, the filtering unit can provide detailed filtering criteria. Furthermore, if the customer is in a hurry, the filtering unit can narrow down and display only the most important criteria. In this way, by adjusting the filtering criteria according to the customer's emotions, more appropriate products can be suggested.

[0112] The output unit can estimate the customer's emotions and adjust the output format based on the estimated emotions. For example, if the customer is feeling stressed, the output unit can output in a simple, highly visible format. If the customer is relaxed, the output unit can output in a format that includes detailed information. Furthermore, if the customer is in a hurry, the output unit can output in a concise format that focuses on the main points. In this way, by adjusting the output format according to the customer's emotions, more appropriate information can be provided.

[0113] The calling unit can estimate the customer's emotions and adjust the timing of the call based on the estimated emotions. For example, if the customer is feeling stressed, the calling unit can quickly call a salesperson. Alternatively, if the customer is relaxed, the calling unit can call a salesperson at an appropriate time. Furthermore, if the customer is in a hurry, the calling unit can immediately call a salesperson. In this way, by adjusting the timing of the call according to the customer's emotions, a salesperson can be called at a more appropriate time.

[0114] The information provision unit can estimate the customer's emotions and adjust the content of the information provided based on the estimated emotions. For example, if the customer is feeling stressed, the information provision unit can provide simple, highly visible information. If the customer is relaxed, the information provision unit can provide detailed information. Furthermore, if the customer is in a hurry, the information provision unit can provide concise information that focuses on the main points. In this way, by adjusting the content of the information provided according to the customer's emotions, more appropriate information can be provided.

[0115] The selection unit can prioritize displaying highly relevant options by taking into account the customer's geographical location information. For example, if the customer is in a specific area, it can prioritize displaying products that are popular in that area. It can also prioritize displaying products that are sold in stores close to the customer's current location. Furthermore, it can display information about promotions and sales that are only available in that area based on the customer's geographical location information. In this way, it is possible to provide highly relevant options by taking into account the customer's geographical location information.

[0116] The narrowing down unit may include an information providing unit that provides product specifications, reviews, and comparison information. For example, the unit may provide technical specifications and performance indicators of the product. It may also provide user reviews and expert reviews. It may also provide comparisons with other companies' products and price comparisons. This allows the customer to check detailed product information.

[0117] At the time of output, the output unit can analyze the customer's past output history and select the optimal output method. For example, it can prioritize the output format (paper, smartphone transmission, etc.) that the customer has used in the past. It can also prioritize the display of specific information from the customer's past output history. Furthermore, it can also suggest the optimal output method based on the customer's output history. This makes it possible to provide the optimal output method by analyzing the customer's past output history.

[0118] When making a call, the calling unit can analyze the customer's past call history and select the optimal calling method. For example, it can prioritize calling methods (button, voice, etc.) that the customer has used in the past. It can also prioritize calls at specific times based on the customer's past call history. It can also suggest the optimal calling method based on the customer's call history. This makes it possible to provide the optimal calling method by analyzing the customer's past call history.

[0119] When providing information, the information provision department can analyze the customer's social media activity and suggest the content of the information to be provided. For example, it can provide information related to products that the customer has "liked" or shared on social media. It can also provide information related to products that the customer's followers or friends have purchased. It can also provide information related to topics or brands that the customer has shown interest in on social media. In this way, it is possible to provide relevant information by analyzing the customer's social media activity.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The selection unit selects customer preferences or required elements. Customer preferences or required elements include, for example, color, size, functionality, and price range. The selection unit provides an interface such as a touch panel, voice input, or two-dimensional code scanning. For example, a capacitive touch panel is used, allowing the customer to make a selection by touching the screen. Voice recognition technology can also be used to accept voice input from the customer. Furthermore, two-dimensional code scanning can be used, allowing the customer to make a selection by scanning a two-dimensional code with a smartphone. Step 2: The filter unit narrows down the products based on the elements selected by the selection unit. The filter unit uses AI to analyze the customer's selection history and past purchase history. For example, it uses a machine learning algorithm to analyze the customer's selection history and suggest the most suitable products. It can also use deep learning technology to analyze the customer's past purchase history and suggest related products. Furthermore, it is possible to set filtering conditions and search for products that match the selected elements. For example, filtering conditions such as price range, brand, and features can be set, and products that match these conditions are narrowed down. Step 3: The output unit outputs the results narrowed down by the narrowing unit. The output unit not only outputs the results on paper, but also has the function of sending them directly to a smartphone. For example, the results can be printed on paper using a printer. The results can also be sent to a smartphone via email or app notification. Furthermore, the system is equipped with a cloud unit that uses a cloud-based database, and can output the results using data stored on the cloud. Step 4: The calling unit calls the salesperson based on the results output by the output unit. The calling unit has a function to send a notification to the salesperson's smartphone. For example, the calling unit sends a notification to the salesperson using a push notification or SMS notification. It can also send an app notification to the salesperson's smartphone.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0124] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0165] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0166] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0177] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0178] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0179] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0183] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0184] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0185] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0186] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0187] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0188] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0190] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0191] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0192] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a selection section for selecting customer preferences or required elements; a narrowing-down unit that narrows down products based on the elements selected by the selection unit; an output unit that outputs the results narrowed down by the narrowing down unit; a calling unit that calls a salesperson based on the result output by the output unit. A system characterized by:

2. The selection unit Provides an interface for touch panel, voice input, and 2D code scanning 2. The system of claim 1.

3. The narrowing section Use AI to analyze customer selection history and past purchase history 2. The system of claim 1.

4. The output unit Equipped with the function to print out the results on paper and send them to a smartphone 2. The system of claim 1.

5. The calling unit Equipped with a function to send notifications to sales staff's smartphones 2. The system of claim 1.

6. The narrowing section An information section that provides product specifications, reviews, and comparison information 2. The system of claim 1.

7. The output unit Equipped with a cloud section that utilizes a cloud-based database 2. The system of claim 1.

8. The selection unit Infer customer sentiment and adjust the order of options based on the inferred sentiment 2. The system of claim 1.

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